medical-imaging-review

name	medical-imaging-review
description	Write peer-review-quality comprehensive reviews for medical imaging AI research (segmentation, detection, classification across CT, MRI, X-ray, ultrasound, pathology). Use this skill whenever the user wants to produce a survey paper, systematic review, literature analysis, or "综述" on deep learning for medical imaging; whenever they mention writing a "review paper" / "literature review" / "系统综述" / "narrative review" / "scoping review" in a medical-AI context; whenever they want a draft suitable for journal submission rather than internal notes; whenever they need help organizing a multi-section method survey with vendor / regulatory / clinical translation coverage. This skill enforces fact-checking, citation integrity, and flagship-review writing voice — NOT a fill-in-the-blank template that invites hallucination. Use it especially when the goal is a publishable manuscript and not just a draft to discuss.
metadata	{"author":"louwill","version":"3.0.0","prior_version_lessons":"v2.0.0 produced drafts that needed extensive multi-day fact-check revision (coronary-cta-paper case): 17 placeholder DOIs, 30-40 [N] drift errors, fabricated method modules, vendor-style citations, 10-subsection flat method taxonomy, AI-tone hedging throughout. v3.0.0 directly addresses these failure modes."}

Medical Imaging AI Literature Review Skill (v3.0.0)

Produce comprehensive reviews that pass first-round peer review on factual grounds, not just structural grounds.

This is not a template-filling skill. It is a write-with-verify discipline.

---

Quick Start

A review project lives in 4 files (3 you write, 1 the skill provides):

project_root/
├── PARADIGM.md            # Style spec from 2-3 exemplar reviews (Phase 0)
├── CLAUDE.md              # Project-specific terminology + literature inventory
├── IMPLEMENTATION_PLAN.md # 3-axis outline + per-claim verification checklist
└── manuscript_draft.md    # The actual manuscript

Follow the 6-phase workflow in references/WORKFLOW.md. The phases are: paradigm capture → init → collect-and-verify → 3-axis outline → write-with-per-claim-verification → multi-agent peer review.

---

Core Principles

Writing voice — match strength to evidence, not hedge by default

Calibrate language to evidence strength, not to a fixed hedging register.

When ≥2 independent peer-reviewed groups confirm a finding, state it strongly. When evidence is single-source or contested, state it cautiously. When evidence is absent, say so.

Avoid the LLM tells:

• "has shown promising results"
• "may suggest"
• "interestingly,"
• "it is worth noting that"
• "in recent years,"
• "demonstrates the effectiveness of"
• "may offer significant advantages"

These phrases are AI-detector top features. Real flagship-review authors don't use them. Strip them.

Take a position when evidence supports it. Neutral catalogue is the LLM default and the failure mode to avoid. See Verdict sentences below.

Citations — every claim verified before commit

Every [N] citation must satisfy four checks:

1. The cited paper exists (DOI / PMID resolves on PubMed or Crossref).
2. The author list matches the first-source (especially first and last author).
3. The numeric claim in the body sentence (Dice, HR, sample size, etc.) appears in the cited paper's abstract or results section.
4. The directional claim in the body sentence (higher/lower, increased/decreased) matches the source's stated direction.

If any check fails, the citation is broken — fix before continuing. See references/CITATION_INTEGRITY.md for the full protocol.

Method descriptions — read first, write after

Do not fill in a template like [Author] et al. [ref] proposed [method]... Achieves Dice of X.XX. That template is a hallucination trap.

Use this discipline instead:

1. Read the actual paper (abstract + methods + results). For arXiv, use read_paper. For closed-access, use Zotero MCP to access the user's library.
2. Note the actual module names, the actual benchmark, the actual numbers, in your own working notes — not in the manuscript yet.
3. Write the method description from those notes, citing specific numbers and module names verbatim from the paper.
4. Verify by spot-checking 1-2 of the numbers against the paper one more time before moving on.

If you can't access the paper, do not write about its internal architecture or specific performance numbers. Cite it for the contribution-level claim only ("first to apply X to Y") and move on.

Heading depth — Nature Reviews uses two levels

• H2 (##) for top-level sections (Introduction, Methods, Applications, Discussion, ...).
• H3 (###) for subsections.
• H4 (####) is forbidden in body. Use bold lead-in **Topic.** paragraph starters for deeper grouping.
• Number prefixes (1., 1.1, 1.2.3) are forbidden in section titles. Nature Reviews / Nat Med / Lancet / JACC don't use them in narrative reviews.

Equations — in a Box, not in body

Display equations (DSC, IoU, clDice, FedAvg, GCN propagation, ...) appear in Boxes, not inline in body paragraphs. Textbook formulas can be referenced ("the Dice similarity coefficient — see Box 1") but should not be displayed inline.

If a formula has no methodological insight worth displaying (e.g., FedAvg averaging), describe it in prose instead of showing it.

Vendor names — only in the regulatory/products table

Vendor names (HeartFlow, Cleerly, Caristo, Keya, Shukun, ...) appear ONLY in the Commercial Products / Regulatory & Validation table. In body text use category descriptors:

• ✗ "HeartFlow's CT-FFR product was validated in NXT, ADVANCE, and PACIFIC..."
• ✓ "The first FDA-cleared CT-FFR product (Table N, row 1) was validated in NXT, ADVANCE, and PACIFIC..."

Reason: scatter-cited vendor names look like marketing copy and undermine the review's authority.

---

Standard Review Structure

# [Title]: <evocative subtitle>

## Key Points
- 4-5 bullets, each 1-3 sentences, expressing the main conclusions.

## Abstract

## Introduction
### Clinical background
### Technical challenge
### Scope and contributions

## Datasets and evaluation metrics
(Table 1: public datasets)
(Box 1: evaluation metrics with equations)

## Methods                              # 3-axis grouping, NOT flat 10-subsection list
### Architectural priors
**CNN-based design.** ... (bold lead-in for sub-grouping)
**Transformer-based design.** ...
**Mamba and state-space design.** ...

### Inductive priors
**Topology-aware design.** ...
**Multi-task design.** ...
**Graph-based design.** ...

### Data regime
**Self-supervised pre-training.** ...
**Foundation models.** ...
**Federated learning.** ...
**Physics-informed models.** ...

(Table 2: representative methods with modality / family / dataset / metric)

## Downstream applications
### [Application 1]
### [Application 2]
### [Application 3]

## Translation to clinical practice
(Table 3: commercial products with regulatory + validation)

## Outstanding challenges

## Future directions

## References

Notes:

• No number prefixes on headings.
• §Methods is 3 H3 subsections (the three axes), with bold lead-ins for each method family inside.
• Tables 1, 2, 3 are typically enough. Box 1 (metrics) is typical. Avoid 5+ tables.
• Verdict sentences cluster at the end of §Methods axis subsections and at the end of clinical translation discussions — not after every paragraph.

---

Verdict Sentences

Each H3 method-axis subsection (Architectural priors / Inductive priors / Data regime) should close with one verdict sentence expressing authorial position. Choose the 3-5 most opinionated positions across the whole manuscript — don't put verdicts on every paragraph.

Verdict templates:

• "[Family] is currently the most cost-effective design choice for [problem]."
• "[Family] has yet to demonstrate clear advantage over [alternative] in clinical-grade evaluations."
• "[Family] is best understood as complementary to [alternative], not a replacement."
• "The next [N] years will determine whether [family] becomes the default backbone or remains a research curiosity."

Neutral catalogue is the LLM default and exactly what flagship review editors push back on. Force yourself to take 3-5 positions.

---

Required Elements

• Key Points box (4-5 bullets, 1-3 sentences each) after the title.
• Tables 1-3: datasets, methods, commercial products.
• Box 1: evaluation metrics with formulas.
• Figures: typically 3-5 (overview/taxonomy, representative architectures, workflow, performance landscape).
• References: cite only what supports the argument. Quantity is downstream of substance — don't pad to a target count.
• Verdict sentences: 3-5 across the whole manuscript, clustered at axis-section ends.

---

Heading Depth

See Core Principles ▸ Heading depth above. Hard rules:

• Max 2 heading levels in body.
• No number prefixes.
• Use bold lead-in **Topic.** for deeper subsubsections.

---

Equations

See Core Principles ▸ Equations above. All display equations go in Box 1 (or rare additional Boxes for specific protocols). Textbook formulas with no methodological insight should be described in prose, not displayed.

---

Vendor Names

See Core Principles ▸ Vendor names above. Vendor names live in Table 3 only; body text uses category descriptors with table cross-reference.

---

Citation Style

# Data citation
"...achieved Dice of 0.730 on ImageCAS [N]"

# Method citation
"Xu et al. [N] introduced..."

# Multi-citation (max 4 in one bracket — beyond that, regroup the claim)
"Multiple groups demonstrated this effect [N1, N2, N3]"

# Comparative
"While [N1] focused on architecture, [N2] addressed the data side"

[N] in body must match the bibliography entry [N], and bibliography [N] must be the paper the body sentence is actually attributing the claim to. See references/CITATION_INTEGRITY.md Rule 3.

---

Literature Sources

Use all three in combination:

Source	Best for	Tools
ArXiv	Methodological preprints, ML/AI advances	mcp__arxiv-mcp-server__search_papers, read_paper
PubMed	Peer-reviewed clinical / validation studies	mcp__pubmed-mcp-server__pubmed_search_articles + WebFetch on PubMed
Zotero	User's local library (closed-access journals)	mcp__zotero__zotero_search_items, zotero_get_item_fulltext
Crossref	DOI verification	WebFetch on api.crossref.org/works/<DOI>

For closed-access journals (Med Image Anal, Eur Radiol, Lancet family) the user's local Zotero library is often the only path. Always check Zotero before assuming a paper is inaccessible.

For MCP server configuration, see references/MCP_SETUP.md.

---

Reference Files

File	Read when
references/WORKFLOW.md	Starting a new review or moving between phases
references/PARADIGM.md	Phase 0: capturing exemplar review style spec
references/CITATION_INTEGRITY.md	Phase 2 (collection) and Phase 4 (write) — every citation must follow the 5 rules
references/HALLUCINATION_PATTERNS.md	Phase 4 (write) and Phase 5 (peer review) — checklist of 9 LLM hallucination indicators to self-check against
references/DOMAINS.md	Phase 3 (outline) — 3-axis method groupings per domain
references/TEMPLATES.md	Phase 1 (init) — CLAUDE.md, IMPLEMENTATION_PLAN.md, table templates
references/QUALITY_CHECKLIST.md	Before delivering a draft to the user
references/MCP_SETUP.md	Setting up arxiv-mcp / pubmedmcp / zotero-mcp

---

Related Skills

For revising an existing AI-drafted review (whether your own previous output or someone else's draft), use ai-review-revision. That skill is the dedicated tool for fixing draft-quality issues — multi-agent diagnostic, factual reset, structural reset, content polish, submission prep.

This skill (medical-imaging-review) is the dedicated tool for producing draft-quality content correctly the first time. They are complementary:

• medical-imaging-review = write-side (produce submission-quality first draft)
• ai-review-revision = revise-side (rescue a draft that already has quality issues)

If a draft produced by this skill still ends up needing the ai-review-revision workflow to land, that's a bug — flag it so this skill can be improved.

---

Why this skill was rewritten (v3 vs v2)

v2.0.0 produced the coronary-cta-paper initial draft. That draft needed extensive multi-day revision before submission-readiness: 17 placeholder DOIs, 30-40 [N] citation drift errors, fabricated method module names with wrong performance numbers, vendor-style citations attributed to peer-reviewed journals, a 10-subsection flat method taxonomy where 3 thematic axes would have served better, AI-tone hedging language throughout.

v3 directly addresses each of these failure modes:

v2 failure	v3 fix
Hedging mandate in Core Principles	Removed; replaced with "match voice to evidence"
80-120 reference count target	Removed; replaced with "cite what supports the argument"
Method fill-in template	Removed; replaced with "read-first, write-after" discipline
10-flat method subsection taxonomy	Replaced with 3-axis grouping in DOMAINS.md
QA = formal structural check	Replaced with per-claim verification embedded in Phase 4
No DOI / author / direction verification	Added as CITATION_INTEGRITY.md with 5 rules
No hallucination self-check	Added as HALLUCINATION_PATTERNS.md (9 patterns)
Numbered headings	Banned; max 2 levels, bold lead-in for deeper
Vendor names scattered	Confined to Table 3 only
Equations inline	Confined to Box 1 only
Verdict-free neutral catalogue	Required 3-5 verdict sentences
No exemplar paradigm capture	Added Phase 0 PARADIGM.md